The outputs of most Machine Learning (ML) systems are often riddled with uncertainties, biased from the training data, sometimes incorrect, and almost always inexplicable. However, in most cases, their user interfaces are oblivious to those shortcomings, creating many undesirable consequences, both practical and ethical. I propose that ML user interfaces should be designed to make clear those issues to the user by exposing uncertainty and bias, instilling distrust, and avoiding imposture. This is captured by the overall concept of Honesty, which I argue should be the most important guide for the design of ML interfaces.